Engineering Aegis Cruiser Topsides ‐ Enhancing Design Capabilities and Life Cycle Support
Bibliographic record
Abstract
ABSTRACT The U. S. Navy is in the process of modernizing ship structures manufactured before 3‐D computer aided design capabilities were available. Detailed 2‐D views on large engineering drawings can be confusing to personnel performing removal and installation activities. 3‐D tools and capabilities would be highly beneficial to the modernization and implementation of the desired improvements by reducing confusion. To help meet this challenge for the cruiser conversion program, it was necessary to capture cruiser topside design model data. This task involved acquiring detailed 3‐D measurements of equipment and structure on the topside of a baseline representative Aegis cruiser on a non‐interfering basis. The 3‐D measurements were achieved by using a 3‐D laser scanner to remotely capture dimensional information of a ship's topside. These measurements have been used to create an As Is 3‐D model of the topside of a baseline representative Aegis cruiser to support cruiser conversion design efforts. The 3D model has provided accurate locations for topside equipment relative to ship structure. This measurement procedure should help in every aspect of topside design by creating more accurate surface combatant topside models. Pedigreed models would aid in studies of antenna coverage, interference, and combat system performance that is sensitive to antenna placement relative to each other and to ship structure. 3‐D laser surveying provides a cost‐effective method of maintaining current topside configuration data for the entire U.S. naval Fleet.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".